Supporting SURgery with GEriatric Co-Management and AI (SURGE-Ahead): A study protocol for the development of a digital geriatrician

Introduction Geriatric co-management is known to improve treatment of older adults in various clinical settings, however, widespread application of the concept is limited due to restricted resources. Digitalization may offer options to overcome these shortages by providing structured, relevant information and decision support tools for medical professionals. We present the SURGE-Ahead project (Supporting SURgery with GEriatric co-management and Artificial Intelligence) addressing this challenge. Methods A digital application with a dashboard-style user interface will be developed, displaying 1) evidence-based recommendations for geriatric co-management and 2) artificial intelligence-enhanced suggestions for continuity of care (COC) decisions. The development and implementation of the SURGE-Ahead application (SAA) will follow the Medical research council framework for complex medical interventions. In the development phase a minimum geriatric data set (MGDS) will be defined that combines parametrized information from the hospital information system with a concise assessment battery and sensor data. Two literature reviews will be conducted to create an evidence base for co-management and COC suggestions that will be used to display guideline-compliant recommendations. Principles of machine learning will be used for further data processing and COC proposals for the postoperative course. In an observational and AI-development study, data will be collected in three surgical departments of a University Hospital (trauma surgery, general and visceral surgery, urology) for AI-training, feasibility testing of the MGDS and identification of co-management needs. Usability will be tested in a workshop with potential users. During a subsequent project phase, the SAA will be tested and evaluated in clinical routine, allowing its further improvement through an iterative process. Discussion The outline offers insights into a novel and comprehensive project that combines geriatric co-management with digital support tools to improve inpatient surgical care and continuity of care of older adults. Trial registration German clinical trials registry (Deutsches Register für klinische Studien, DRKS00030684), registered on 21st November 2022.

-Evaluation of physical, functional, social, and psychological parameters relevant to geriatric comanagement and follow-up decisions in the inpatient setting.
-Use of the collected data in the further course of the project to program an artificial intelligence (AI) that generates a proposal for the best possible aftercare facility and to analyze the current standard of care.

Population
Patients hospitalized for surgery ≥ 70 years of age with an ISAR 1 score ≥ 2

Goal
Collection of a data set -For training the AI for the follow-up recommendation of the dashboard.
-As a comparison cohort for the intervention study planned in the course (SURGE-Ahead years 4-6) (=representation of the current standard of care).

Outcomes
Primary: Expert judgment for best follow-up option at discharge & verification at followup. The following follow-up options will be recorded: -Geriatric acute care clinic

Background
Geriatric co-management of surgical patients can improve treatment, reduce the severity of long-term sequelae, and reduce mortality (1)(2)(3). The integration of geriatric expertise has been particularly successful so far in trauma surgery (4). But also in other surgical specialties, such as general surgery (5) or urology (6) patients can benefit from geriatric co-management.
A core aspect of the geriatric treatment approach is the holistic view of patients through a comprehensive geriatric assessment (CGA) performed by a multidisciplinary team. In a CGA, different domains are considered in order to identify physical, psychological, social, and functional limitations and to include them in the treatment (7,8).
However, in view of the increasing number of geriatric patients and the shortage of trained geriatricians, geriatric co-management has not yet been established on a widespread basis, despite its advantages. A particular challenge in the care of geriatric patients is the assessment of the optimal follow-up path (e.g., discharge to home, to a nursing home, to a geriatric rehabilitation clinic or to a geriatric acute care clinic). The SURGE-Ahead project will develop a digital application (dashboard) for improving geriatric co-management in surgical hospitals. Instead of a CGA, a dataset will be defined that maps the minimum requirements for successful geriatric co-management (Minimum Geriatric Dataset -MGDS). Based on the MGDS, the dashboard will display suggestions for 1) evidence-based treatment options of typical geriatric diseases and syndromes based on simple algorithms and 2) suggestions for optimal follow-up care based on artificial intelligence (AI). The dashboard will provide geriatric expertise in surgical clinics. In operation, the system is designed to support the entire multidisciplinary team by providing an initial assessment and treatment recommendations. The goal is to sustainably improve treatment and continuing care for older patients.
SURGE-Ahead was launched in July 2021. The first three years of the project are dedicated to the development of the dashboard. Based on evidence and expert consensus, the MGDS will be defined for the operation of the dashboard. The MGDS is composed of 1) pre-and post-operative assessments and questionnaires (see Section 7.1), 2) existing data from the hospital and laboratory information systems, and 3) data on movement and mobility parameters collected via a body sensor (Axivity AX6).

Rationale for the study to be conducted
In the planned observation and AI development study (OKIE), the data for the training and development of the AI will be collected in three clinics of the University Hospital Ulm (trauma, hand, plastic and reconstructive surgery, general and visceral surgery, urology, and pediatric urology) with

Risk-benefit analysis
The assessments to be carried out mean an additional effort of just under two hours for study participants. This time is divided into preoperative and postoperative assessments as well as a 30minute telephone follow-up approximately 90 days after discharge. There is no risk that the assessments will exceed the risks of the patients' current individual hospital treatment. If the survey is a burden for the patient, it will be interrupted immediately and continued at a later time, if necessary.
All participants can withdraw from the study at any time and without giving reasons. The assessments are performed by trained personnel (study nurses, study physicians). During the inpatient study phase, the study physicians and study nurses s are available to answer questions.
If desired, the knowledge gained can be passed on to the test persons after the study has been completed. In addition, the test persons make a valuable contribution to the scientific knowledge gained.

Study goals
During the OKIE, the data set defined for SURGE-Ahead (MGDS) will be collected for 170 to 240 patients at the three participating hospitals. In addition to the MGDS, a physician experienced in geriatrics will document a recommendation for a follow-up option for all subjects before discharge from the hospital based on the collected MGDS data, the medical record, and a personal patient contact. This recommendation is verified again at follow-up, corrected if necessary, and established as the gold standard. This is used to train the AI to generate a suggestion for the best possible discharge destination in the final dashboard.
Thus, the primary goal of OKIE is to collect a dataset for training the AI to generate a recommendation for a post-acute care facility.
The secondary goal is to use the OKIE data set as a comparison cohort for the intervention study planned during the course (SURGE-Ahead year 4-6) with the completed dashboard (=representation of the current standard of care).

Study design
A prospective observational study with a follow-up after three months will be conducted in the clinics for trauma, hand, plastic and reconstructive surgery, general and visceral surgery and urology and pediatric urology of the University Hospital Ulm. There is no intervention, so there is no active involvement in the regular care delivery during the study.
Important data for the treatment and for the evaluation of the treatment success of geriatric patients will be collected at different assessment points and during the follow-up (MGDS). Based on the collected MGDS data, the medical record and a personal patient contact, a physician experienced in geriatrics from the project team will also document an expert assessment for an optimal follow-up option for all subjects. This assessment will be verified again at follow-up by the same geriatrician and will serve as a reference for the training of the AI. The assessment of the geriatrician is not shown to the treating clinical staff and has no influence on the continuation of care of the subjects.
According to the study design, blinding of the study team is not possible and not necessary since no intervention takes place.

Inclusion criteria
• Patients ≥ 70 years of age admitted for an inpatient stay with surgical intervention in one of the three participating hospitals and whose surgery has not yet been performed (emergency or elective admissions).

Exclusion criteria
• Patients in a palliative treatment situation (life expectancy < 3 months based on clinical assessment by the treating physician).
• Patients who are incapable of giving consent and for whom no legal guardian or authorized person is available.
• Collection of assessments not possible due to limited ability to communicate (e.g., due to lack of language skills).
• Patients already participating in another study.
• Patients with a presumed length of stay of < 3 nights.

Calculation of sample size
A larger dataset improves the performance of the AI being developed. If the dataset is too small, there is a risk that the AI will not be adequately trained, but will make the decision largely by chance (11).
Starting from a minimum number, a recruitment corridor is therefore aimed at, which can be exhausted if recruitment progresses well. With an expected dropout rate of 20%, 170 -240 patients are to be recruited. The focus here will be on the Department of Trauma, Hand, Plastic and Reconstructive Surgery, as this is where the greatest range and variability of possible follow-up options is covered, and patients treated here are likely to have the greatest benefit from a subsequent dashboard application. The following breakdown is targeted (may be slightly adjusted as the study progresses): -UCH: 120 -190 subjects. The target case numbers are based on an assessment of feasibility regarding AI training and recruitment, no explicit case number calculation was performed.

.1 Study duration
The study is planned for 12 months from Feb 1, 2023, to Jan 31, 2024, with a recruitment period of nine months and a follow-up of three months. If the recruitment goal is not reached within the nine months, the recruitment period can be extended by another three months. In this case, the study will run with follow-up until 30.04.2024 at the latest.

Study preparation
The study will be prepared between Dec 1, 2022, and Dec 31, 2022. The study nurses, physicians, nursing staff and the data manager will be briefed on the study and the study procedure. At least one information session will be held per clinic, during which there will also be an opportunity to ask questions. The aim is to prepare the local clinical staff as well as possible for the start of the study to gain their support in the selection and recruitment of suitable patients.
On site in the clinics, the study nurses are the contact persons for acute questions. Here, care is taken to ensure as comprehensive a presence as possible. In addition, physicians are available in all three clinics (see chapter 2.3), who are also part of the SURGE-Ahead study team.

Recruitment of the test persons
Screening with the ISAR score is an official recommendation and is well known in the centers. In most cases, the score is collected as standard at admission. For the OKIE, all patients 70 years of age and older who are admitted to UCH, AVC or URO for an inpatient surgical procedure will be screened with the ISAR. Recruitment of potential subjects will occur after confirmation of inclusion criteria. The verification of the inclusion criteria is paper based in favor of an easier patient contact. After inclusion, the data are subsequently recorded electronically (see also section 7.2).
Potential subjects will be informed about the possibility of participating in the SURGE-Ahead project immediately after hospital admission or, in the case of elective surgery, during a preliminary discussion. In case of interest, the patients will be informed orally and in writing by the study physicians about the aims, procedures, risks, and data protection of the research project and will receive written patient information. This also includes information about the use of a lumbar sensor (company Axivity AX6®) for the collection of postoperative mobility data during the inpatient stay.
All patients have the opportunity to ask questions during the information session. Consent to participate in the study is given verbally and in writing and is voluntary.
If the respondents are too stressed by the situation due to their underlying disease (e.g., fracture) and/or are unable or unwilling to continue the interview for other reasons, they can interrupt the interview at any time. In this case, it is also possible for some data to be collected by a caregiver (e.g., relatives, friends) via external anamnesis.
In the follow-up survey (T6), the respondent's family doctor will also be asked about the quality of follow-up care. To enable the questioning of relatives and family physicians, the test persons give a release of confidentiality. The required contact data for relatives and general practitioners are first recorded in the release from confidentiality and later transferred to the paper-based identification list together with the respondent's identification number. The release from confidentiality is requested upon enrollment in the study but can also be signed during study participation. However, without a signed release of confidentiality, no survey of the primary care physician can be conducted during the follow-up.
After patient consent, the patient is enrolled in the study. For documentation of patient selection and recruitment progress, an anonymized screening list is maintained in all centers, including any reasons for exclusion.

Recruitment of cognitively impaired subjects
According to the Declaration of Helsinki as amended in October 2013 (taking into account points 28.-30.), the inclusion of non-consenting individuals in medical studies is only permissible under narrow conditions (12). These provide that the patient 1) either derive a direct benefit of their own from participating in the study, or 2) the group of patients that this individual represents is likely to benefit from the findings of the study and the study presents minimal risks and burdens. In the case of SURGE-Ahead, case 2) applies. Current evidence from the Geriatric Traumatology Center (ATZ) Ulm demonstrates that the group of cognitively impaired patients is particularly vulnerable to perioperative complications, mortality, or persistent functional deficits (13). They represent about 40% of the patients cared for. Thus, this group would particularly benefit from geriatric co-management and optimal follow-up decisions.
If the patient is found to be incapable of giving consent during the information session, information and consent will be obtained from the legal guardian or proxy.
If a patient is incapable of giving consent and no legal guardians or proxies are available, participation in the study is not possible (exclusion criterion). Also, when the power of attorney or care directive cannot be presented, study participation is not possible. Figure 1 shows the recruitment procedure for patients with cognitive impairment.

Examination times
There will be up to seven data collection time points, T0-T6, depending on the length of stay: -T5: 1-2 days before discharge -T6: Follow-up 90 days after discharge (± 7 days).
In addition, there are surveys that are conducted after discharge, but are not tied to a fixed time period ( Figure 2).
After inclusion of the subjects, the preoperative assessments (T0) are performed (see Chapter 7). Some assessments are performed preoperatively for all subjects (T0.1). These assessments can be performed 0-3 days before surgery. If surgery is postponed for more than three days, the preoperative assessments must be repeated. To consider the possibly lower resilience of participants preoperatively, especially considering the longer assessment section T0.2, it is possible to collect a part of the preoperatively planned assessments, if necessary, postoperatively or through an external history. On days 1, 3, 5 and 7 after surgery, the postoperative interviews and assessments will be performed (T1-T4). Here, the study nurse first looks at the patient's file and the system data (see below) and records relevant data. Subsequently, the interview with the patients takes place. On day 1 after surgery, the mobility sensor (Axivitiy AX6®) is attached (see chapter 7.1.5). If patients are discharged before day 7 after surgery, the postoperative assessments will only be performed until the discharge day. At discharge, the system data will be recorded again by the study nurse. In addition, 0-3 days before discharge, the expert assessment will be performed by the geriatric experienced physician based on the collected MGDS data, the medical record and a personal patient contact including performance of the discharge assessments (T5). This assessment is used exclusively for the training of the AI and has no influence on the actual treatment of the patient. Following discharge, a retrospective recording of adverse events and complications, as well as a critical assessment of the medical discharge report regarding completeness and quality (time-independent) is carried out.
Parallel to the assessments, which are completed together with participants, data from the hospital information system (HIS), the patient file, and the laboratory information system (LIS) are recorded manually. The master data from the HIS are recorded once. Clinical HIS data (e.g., medication, diagnoses) and the data from the LIS are recorded preoperatively and on days 1, 3, 5 and 7 after surgery and at discharge, or checked for new entries and updated (T1-T5).
90 days ( 7 days) after discharge, a telephone follow-up (FU) is conducted (T6). This takes about 30-40 minutes (see chapter 7). In the case of patients who are not capable of giving consent, the caregiver is preferably interviewed. However, if this person is not a member of the family (professional caregiver), the patient is interviewed. In addition, an attempt is made to obtain the assessment of the family doctor. Figure 2 shows an overview of the survey times. The MGDS consists of 1) parameters extracted from existing systems or files (hospital information system, laboratory information system, anesthesia sheet, patient file), 2) data collected by means of validated assessments and questionnaires, and 3) sensor data on mobility aspects. In addition, the assessment of the physician experienced in geriatric medicine is also collected as part of the OKIE. Table 2 lists all data, examination times and possible sources.
For the assessments and questions with patient contact listed in Table 2, we assume a total time  Table 2.  3 To be collected as Patient Reported Outcome (CHARMI-PROM). *If necessary, to be collected by relatives or caregivers via external anamnesis (with signed release from confidentiality) #If necessary, to be collected from the patient's file.

Existing data sources
Many data, such as information on weight, height, decubiti, adverse events and complications, medication (incl. frequency and dose), alcohol/smoking behavior, and laboratory parameters are already routinely collected and documented at different locations/in different systems. These sources will be reviewed first to minimize the burden on the subject. Explicitly, the anesthesia sheet, the patient chart, the medication schedule, the electronic patient record in the hospital information system (HIS), and the laboratory information system (LIS) will be reviewed. If the data cannot be extracted from existing sources, the subjects will be asked. The source of the data is documented in all cases.

Assessments and questionnaires
The biggest part of the MGDS consists of clinical assessments covering the most important domains of geriatric patient care (e.g., cognition, mobility, pain, continence, activities of daily living or comorbidities). In addition, the German version of the "Client Sociodemographic and Service Receipt Inventory (CSSRI)" (16,17) (adapted to project requirements) will be used at follow-up T6 to capture health service utilization for the health economic evaluation. Some assessments and questions will be collected multiple times to allow for progress documentation. If necessary, individual assessments and questions can also be collected from relatives via an external history (see Table 2).

Geriatric expert assessment for the discharge destination
The expert recommendation by a physician experienced in geriatrics is made at T5 (discharge) and at T6 (follow-up). In this context, a recommendation for the best possible follow-up option is documented based on the assessment data of the MGDS collected at discharge from the acute hospital, the medical record, and a personal interview with the subjects (no interaction with the clinical treatment team takes place here). This recommendation is then reconfirmed or adjusted at the follow-up appointment based on retrospective observation of the further course. In case of long duration or acute deterioration after T5, the expert assessment may be repeated.

Time-independent review of the patient file
The needs of geriatric patients are often complex and interdisciplinary. To analyze the course of inpatient treatment of the participating subjects regarding the need for geriatric co-management, various contents of the patient file will be critically analyzed. This analysis will be carried out retrospectively and independent of the inpatient treatment of the test persons.  Among others, the freely available GGIR package (Raw Accelerometer Data Analysis) for R is used for the analysis (20). To test these and possibly other algorithms for the OKIE patient collective, 30 participants (N=10 per recruiting clinic) will complete a movement diary while wearing the sensor (T1 -T4). Cognitively impaired participants are excluded from this. Here, among other things, the times of getting up in the morning and going to bed in the evening are recorded.

Data acquisition
Data collection is performed by study nurses. One position per clinic is planned for this purpose (they should also support each other). All three clinics and the study nurses will be equipped with a 2in1 device (laptop and tablet) from Dell for data collection. The device is operated in the secure hospital network and has access to the HIS and LIS. It is operated in cooperation with the Center for Information and Communication of the University Hospital Ulm and meets the strict IT requirements for operation in the network of the University Hospital Ulm.
A dedicated data entry application was programmed for OKIE, which is hosted within the network of the University Hospital Ulm and can be accessed via any standard browser (e.g., Edge, Chrome, Firefox) within the network. For data entry, the study nurses call up the application in their browser via a secure connection and dial into the interface using their individually assigned access data. The data are entered by the study nurses using preset input masks adapted to the standardized questionnaires (see can be collected without the participation of the subjects or their relatives (i.e., data from the HIS or LIS) are extracted by the study nurses at a workstation in the University Hospital Ulm and entered into the input mask. Before each assessment, the conditions during the interview and the isolation status of the patient are recorded. All data entered via the input mask are stored in a password-protected database located on the same server as the application.
Regular project monitoring is used to continuously monitor the progress of the project. Data quality is also checked to identify problems in good time and to be able to take countermeasures.

Data evaluation
Primarily, the goal of the current OKIE is to collect a training data set for the AI. In years 4-6 of the SURGE Ahead project, an intervention study is planned to test the dashboard in clinical practice.
Therefore, the OKIE will also serve as preparation as well as a comparison cohort for the evaluation of the intervention. Key outcome parameters are listed below.

Programming the AI for the follow-up recommendation
The goal of the entire SURGE-Ahead project is to develop a digital application that maps perioperative geriatric co-management and makes a recommendation for appropriate follow-up care facilities. For this second aspect, we currently assume the following main categories (classes) in the German In general and visceral surgery and urology, project partners estimate that approximately 70-90% of patients are discharged home.
To predict the discharge destination as accurately as possible, sufficient data points are required for each COC destination (class). The exact number of data points needed in total and per class varies with the model used (e.g., linear and non-linear) and the targeted performance. Since the model is determined post-hoc and exploratory, the goal is to distribute the data points as evenly as possible across classes. A data set is needed in which the categories to be predicted occur often enough (11).
Therefore, the initial goal for OKIE is a four-category approach, namely discharge 1) to an acute geriatric hospital, 2) to a rehabilitation hospital, 3) to a nursing home, and 4) to home. If possible, further calculations are then performed with the collected dataset for refinement.

Description of the current standard of care
The OKIE dataset captures the current standard of care and is intended to assess the need for geriatric co-management with respect to various primary and secondary outcomes. Currently, descriptive statistical methods are planned to describe the different endpoints. This also includes health economic considerations. The cost-effectiveness ratio of the resources used will be determined from an economic perspective based on the utilization of health care services (CSSRI) and quality of life (EQ 5D 5L) using the net benefit method. The cost of illness is estimated by multiplying the units of services used by the determined costs of these units, each for a period of 3 months (T0 to T6). For the intervention study planned in a future project phase, the collected data will be used as a comparison cohort, if necessary. For this intervention study, a separate ethics application will be submitted in the course.

Completing the data set
The data collected will be used to calculate cross-cutting scores and verify them with the data so that they can be used in the planned intervention study if necessary. These include, for example, the Nottingham Hip Fracture Score for predicting 30-day mortality after hip fracture. The score is based on the following seven parameters: age, sex, number of comorbidities, cognitive status, preoperative 8 Adverse events (AE) / serious adverse events (SAE) 8

.1 (S)AE for participants
If AEs or SAEs occur during the interview, the study headquarters will be informed of the incident.
Subsequently, it is checked whether there is a connection with the study. If this is the case, the responsible ethics committee will be informed within 7 working days. Optimal therapy and care can be always ensured by the clinical treatment team on site. Furthermore, in case of adverse events, geriatric expertise from the study team can be accessed at any time.

Adverse events that jeopardize the success of the study
To be prepared for possible contingencies and adverse events that could jeopardize the progress and success of the study, various measures were defined. These are recorded in Table 3.  9 Ethical and legal aspects 9.1 Consent All participants will be informed verbally and in writing about the study objectives, the procedure, possible risks, and the use of the data (see Appendix: Information for patients). Participants will be explicitly informed that they can contact the study team if they have any problems or questions, or that they can also terminate their participation in the study at any time. The contact information of the study staff (telephone number, e-mail address) will be provided to the participants with the educational documents.
Consent is obtained for participation in the study, for the external medical history to be taken by relatives/caregivers and general practitioners (release from obligation to maintain confidentiality), and for the recording of mobility data by the sensor. The participants confirm their consent with their signature during the educational interview.
For patients who are not capable of giving consent, a legal guardian is consulted (see also Chapter 6.4).

Costs and compensation for participants
There are no costs for the participants. Participants do not receive any monetary or other compensation.

Risks for participants
The risks for the participants are low. If the interview is too stressful for the patients, it will be interrupted immediately and continued later if necessary. Since we believe that this could happen in the preoperative assessments (especially in the case of emergency patients), only the four most important domains are queried here (identification of geriatric patients (ISAR), delirium (4AT), ADL (Barthel), pain (NRS-P)). The ISAR score is already routinely collected in most cases at Ulm University Hospital. The pain scale and the Barthel index are used in all clinics. Since the ISAR score is part of the screening, usually only the 4AT test is added preoperatively. An additional time expenditure of about two minutes is to be expected for this.
The risk of psychological complaints being caused by participation is considered to be low. If necessary, the longer preoperative assessment period (T0) can be divided into 2 periods (T0.1 and T0.2); of course, a withdrawal from the voluntary participation in the study is possible at any time.
The risk of Sars-Cov2 infection through contact with study personnel is also considered to be low. All study personnel are required to undergo regular testing using rapid antigen tests and to wear an FFP2 mask for the duration of their stay at Ulm University Hospital. The study personnel will also be provided with sufficient disinfectant. In addition, the University Hospital Ulm has its own, comprehensive hygiene concept, according to which the study personnel must comply. Any adaptations to the hygiene concept of Ulm University Hospital that may take place in the further course of the study (e.g., higherfrequency rapid antigen testing for employees) will also be implemented by the study personnel.
All participants can withdraw from the study at any time and without giving reasons.

Benefit
If desired, the data collected from and about them can be passed on to the participants after completion of the study. In addition, the test persons make a valuable contribution to the scientific knowledge gained.
During the inpatient study phase, the study physicians and nurses are available to answer questions.

Insurance
During participation in the study, all subjects are covered by insurance. applicable, the date of discontinuation, are documented for all subjects. The paper-based identification list is the only way to assign records in the study database to an individual. The data in the list are used to identify participating patients, to clarify ambiguities or to hand out the recorded data to the subjects at their request. The identification lists remain in the respective clinics in locked cabinets in rooms with restricted access. The identification lists are updated and maintained by the authorized study personnel on site.

Data acquisition via the input mask
Data is collected via a specially programmed user interface. As mentioned in chapter 7.2, communication between the study nurse and the application is encrypted and password protected.
The data entered is stored in a password-protected database, from which a regular backup is created.
The data remains only within the network of the University Hospital until the study is completed.

Data acquisition by the AX6® sensors
All collected data is stored locally on the memory card of the sensor. At the end of the wearing period, the sensor is removed, and the data read out by the study nurse. The sensor data are initially stored in a separate database, provided with the unique study identification number. Only after the raw data have been evaluated are they fed into the study database. The sensor is hygienically cleaned, loaded, recalibrated and can subsequently be used again in the study.

Retention and archiving of data
The study-related, pseudonymized data remain on the server of the University Hospital Ulm until the end of the study. Afterwards, the data are transferred on a secure data carrier (e.g., USB stick) to the study center at the AGAPLESION Bethesda Clinic Ulm, where they are transferred to the server of the Institute of Geriatric Research and stored in a cloud operated by the institute. This is protected from unauthorized access by a web application firewall (WAF). Access is only possible through 2-factor authentication by authorized employees of the Institute. Data is stored for at least 10 years in accordance with applicable law. Informed consent forms will remain in the respective clinics and will be stored separately from the study data, also for 10 years, in a locked cabinet, in a locked room suitable for data storage. The study participant identification list with the pseudonymization codes of the study participants will also remain in the respective clinic and will be kept securely locked together with the consent forms. The identification lists will be destroyed after completion of the study and complete data cleaning. Data cleaning will be performed by authorized study personnel of the University and the University Hospital Ulm.

Data protection and duty of confidentiality
All employees involved in the project are trained in advance on how to handle the collected study data responsibly and to comply with the data protection guidelines and undertake to do so in writing when they join the project. All medical project staff are subject to medical confidentiality.